Detection of pathologies in ocular images

ABSTRACT

A computer-implemented method of searching for a region indicative of a pathology in an image of a portion of an eye acquired by an ocular imaging system, the method comprising: receiving image data defining the image; searching for the region in the image by processing the received image data using a learning algorithm; and in case a region in the image that is indicative of the pathology is found: determining a location of the region in the image; generating an instruction for an eye measurement apparatus to perform a measurement on the portion of the eye to generate measurement data, using a reference point based on the determined location for setting a location of the measurement on the portion of the eye; and receiving the measurement data from the eye measurement apparatus.

FIELD

Example aspects herein generally relate to the field of image processingand, more particularly, to the processing of an ocular image tofacilitate identification of the presence and/or location of a pathologyin an imaged portion of an eye.

BACKGROUND

An ocular image of a portion of a subject's eye acquired by an ocularimaging system may have one or more regions containing some indicationof a pathology that would not be present in a comparable image of ahealthy eye, and the presence of such region(s) may be indicative of adisease or an otherwise unhealthy state of the subject. By way of anexample, a fluorescein angiogram of the subject's retina may contain oneor more regions where blood vessel leakage appears to have occurred,which can be a sign of macular oedema. Ocular images require anexperienced ophthalmologist to acquire and assess. However, existingtechniques are time-consuming for the ophthalmologist and/or thepatient, and the assessment performed may be prone to error.

SUMMARY

The present inventors have devised, in accordance with a first exampleaspect herein, a computer-implemented method of searching for a regionindicative of a pathology in an image of a portion of an eye acquired byan ocular imaging system. The method comprises receiving image datadefining the image, and searching for the region in the image byprocessing the received image data using a learning algorithm trained onimage data defining images of the portion of healthy eyes, and imagedata defining images of the portion of unhealthy eyes each having atleast one region that is indicative of the pathology. In case a regionin the image that is indicative of the pathology is found in thesearching, a location of the region in the image is determined, aninstruction is generated for an eye measurement apparatus to perform ameasurement on the portion of the eye to generate measurement data,using a reference point based on the determined location for setting alocation of the measurement on the portion of the eye, and themeasurement data is received from the eye measurement apparatus.

The present inventors have also devised, in accordance with a secondexample aspect herein, a computer-implemented method of searching forthe presence of a pathology in an image of a portion of an eye acquiredby an ocular imaging system. The method comprises receiving image datadefining the image, and searching for the presence of at least one of aplurality of different types of pathology in the image by processing thereceived image data using a learning algorithm trained on image datadefining images of healthy eyes, and images of unhealthy eyes eachhaving a respective one of the different types of pathology. The methodfurther comprises, in case at least one of the plurality of differenttypes of pathology is found to be present in the image, processes of:selecting, for each of at least one type of pathology found to bepresent in the image, a respective one of a plurality of different typesof measurement modality which is to be used to perform a measurement onthe portion of the eye; generating, for each of the at least one type ofpathology found to be present in the image, a respective instruction foran eye measurement apparatus of the respective selected measurementmodality to perform the measurement on the portion of the eye; andreceiving measurement data of the measurement performed by the eyemeasurement apparatus of each selected measurement modality.

The present inventors have further devised, in accordance with a thirdexample aspect herein, a computer program which, when executed by acomputer, causes the computer to perform the method according to thefirst or the second example aspects herein.

The present inventors have further devised, in accordance with a fourthexample aspect herein, a non-transitory computer-readable storage mediumstoring the computer program according to the third example aspectherein.

The present inventors have further devised, in accordance with a fifthexample aspect herein, a signal carrying the computer program accordingto the third example aspect herein.

The present inventors have further devised, in accordance with a sixthexample aspect herein, an apparatus for searching for a regionindicative of a pathology in an image of a portion of an eye acquired byan ocular imaging system. The apparatus comprises a receiver moduleconfigured to receive image data defining the image. The apparatusfurther comprises a search module configured to search for the region inthe image by processing the received image data using a learningalgorithm trained on image data defining images of the portion ofhealthy eyes, and image data defining images of the portion of unhealthyeyes each having at least one region that is indicative of thepathology. The apparatus further comprises an instruction generatingmodule configured to perform, in response to a region in the image thatis indicative of the pathology being found by the search module,processes of: determining a location of the region in the image; andgenerating an instruction for an eye measurement apparatus to perform ameasurement on the portion of the eye to generate measurement data,using a reference point based on the determined location for setting alocation of the measurement on the portion of the eye. The receivermodule is further configured to receive the measurement data from theeye measurement apparatus.

The present inventors have further devised, in accordance with a seventhexample aspect herein, an apparatus for searching for the presence of apathology in an image of a portion of an eye acquired by an ocularimaging system. The apparatus comprises a receiver module configured toreceive image data defining the image and a search module configured tosearch for the presence of at least one of a plurality of differenttypes of pathology in the image by processing the received image datausing a learning algorithm trained on image data defining images ofhealthy eyes, and images of unhealthy eyes each having a respective oneof the different types of pathology. The apparatus further comprises aninstruction generating module configured to perform, in response to atleast one of the plurality of different types of pathology being foundto be present in the image by the search module, processes of:selecting, for each of at least one type of pathology found to bepresent in the image, a respective one of a plurality of different typesof measurement modality which is to be used to perform a measurement onthe portion of the eye; and generating, for each of the at least onetype of pathology found to be present in the image, a respectiveinstruction for an eye measurement apparatus of the respective selectedmeasurement modality to perform the measurement on the portion of theeye. The receiver module is further configured to receive measurementdata of the measurement performed by the eye measurement apparatus ofeach selected measurement modality.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be explained in detail, by way ofnon-limiting example only, with reference to the accompanying figuresdescribed below. Like reference numerals appearing in different ones ofthe figures can denote identical or functionally similar elements,unless indicated otherwise.

FIG. 1 is a schematic illustration of an apparatus for searching for aregion indicative of a pathology in an ocular image, in accordance witha first example embodiment herein.

FIG. 2 is a block diagram illustrating an example signal processinghardware configuration of the apparatus of FIG. 1, according to anexample embodiment herein.

FIG. 3 is a flow diagram illustrating a process by which the apparatusof FIG. 1 searches for a region indicative of a pathology in an ocularimage, in accordance with the first example embodiment herein.

FIG. 4(a) is a schematic illustration of an image of a portion of theeye acquired by an ocular imaging system.

FIG. 4(b) is an annotated version of the image shown in FIG. 4(a),showing a region which is indicative of a pathology that has been found,in accordance with an example aspect herein.

FIG. 5 is a schematic illustrating how a learning algorithm may betrained on image data defining images of the portion of healthy eyes,and image data defining images of the portion of unhealthy eyes eachhaving at least one region that is indicative of a pathology, inaccordance with a first example aspect herein.

FIG. 6 is a schematic illustrating how a learning algorithm may betrained on image data defining images of a portion of healthy eyes, andimage data defining images of the portion of unhealthy eyes each havingat least one region that is indicative of a respective one of aplurality of different types of pathology, in accordance with a secondexample aspect herein.

FIG. 7 is a schematic illustration of a convolutional neural networkcomprising artificial neurons in an input layer, a hidden layer, and anoutput layer.

FIG. 8 is a flow diagram illustrating a process by which a search moduleof the apparatus of FIG. 1 may search for a region indicative ofpathology in the image, according to an example embodiment herein.

FIG. 9 is a schematic illustration of an apparatus for searching for thepresence of a pathology in an ocular image, in accordance with anexample embodiment herein.

FIG. 10 is a flow diagram illustrating a process by which the apparatusof FIG. 9 searches for the presence of a pathology in an ocular image,in accordance with an example embodiment herein.

DETAILED DESCRIPTION OF EMBODIMENTS

In certain example embodiments described herein, an ocular image isprocessed to automatically detect a pathology in the ocular image usingthe image processing techniques hereinafter described, and then afurther measurement on the eye is automatically instructed in order toacquire supplementary data that can allow the presence of the pathologyto be determined (and/or to allow the pathology to be characterised)with a higher degree of confidence. For example, the eye may be imagedusing a different imaging modality (for example, optical coherencetomography, OCT), or its functional response to light stimulationmeasured. By way of further example, in the case of a fluoresceinangiogram in which the image processing detects a possible indicationthat blood vessel leakage has occurred in a region of the retina, an OCTscan of the region may be instructed, and thickness or volumemeasurements may be obtained from the data of the OCT scan in order toconfirm the presence of the macular oedema and, where present,optionally diagnose its severity. Embodiments can thus facilitate fastand reliable detection of pathologies for early detection of disease,their advantages being most pronounced where the ocular image is awide-field or ultra-wide-field image covering a large part of the retinaor other portion of the eye.

Embodiments of the present invention will now be described in detailwith reference to the accompanying drawings.

Embodiment 1

FIG. 1 is a schematic illustration of an apparatus 100 according to afirst example embodiment, for searching for a region indicative of apathology in an image of a portion of an eye acquired by an ocularimaging system (shown at 520 in the schematic illustration of FIG. 5).The apparatus 100 comprises a receiver module 110 configured to receiveimage data defining an image produced by a ocular imaging system 520,and a search module 120 configured to automatically search for at leastone region indicative of a pathology in the image, by processing thereceived image data using a learning algorithm which is described indetail below. The apparatus 100 further comprises an instructiongenerating module 130 configured to automatically perform, in responseto a region in the image that is indicative of the pathology being foundby the search module 120, processes of: determining a location of theregion in the image and generating an instruction for an eye measurementapparatus to perform a measurement on the portion of the eye to generatemeasurement data, using a reference point based on the determinedlocation for setting a location of the measurement on the portion of theeye. In cases where two or more such regions are found by the searchmodule 120, these processes may be performed for each of the regions.The receiver module 110 is further configured to receive the measurementdata from the eye measurement apparatus. Optionally, the apparatus 100may, as in the present illustrated embodiment, comprise a displaycontrol signal generator 140 (this optional component being shown by thedashed lines in FIG. 1).

A region indicative of a pathology may be a region of the image definedby the received image data which contains image features indicative of alesion, structural damage or any other abnormality that would not bepresent in a similarly-acquired image of a healthy eye.

The apparatus 100 may be configured to search for a region indicative ofany pathology that may occur in the eye, which may include (but is notlimited to), for example, glaucoma, moderate diabetic retinopathy,severe diabetic retinopathy, tumour, drusen, oedema and atrophy.

In the present example embodiment, the receiver module 110 is configuredto receive image data defining an image of a portion of a retina of theeye, which has been generated by the ocular imaging system 520. However,in other example embodiments, the received image data may define animage of a portion of the eye other than the retina, for example aportion of the anterior segment of the eye, or a portion of theposterior segment of the eye. Furthermore, the received image data may,as in the present example embodiment, define a two-dimensional image, orit may alternatively define a three-dimensional image of the imagedportion of the eye. The received image data may be provided in anysuitable format (whether compressed or uncompressed) known to thoseskilled in the art. The image data received by the receiver module 110is of a first imaging modality (discussed in more detail below), andrepresents a result of imaging the retina of the eye using appropriatelyselected values for imaging parameters, which may include imagingresolution, aperture size, and wavelength.

The ocular imaging system 520 may be any ocular imaging system that issuitable for imaging the retina (or other selected portion) of the eye.The ocular imaging system 520 may, for example, be a fundus camera, or atype of scanning imaging system. By way of example, the ocular imagingsystem 520 of the present example embodiment is a scanning imagingsystem in the exemplary form of a scanning laser ophthalmoscope (SLO),which is configured to acquire images of the retina of a subject's eye.The SLO of the present example embodiment is configured to captureautofluorescence (AF) images (it may be configured to capture Red-Green(RG) reflectance images or images from other fluorescence modes),although it may alternatively or additionally be configured to acquireone or more other types of images. The SLO may, for example, be anultra-wide field SLO (UWF-SLO) capable of generating an ultra-wide fieldimage of up to 80% of a retinal surface. Alternatively, the ocularimaging system 520 may be of another imaging modality, for example anoptical coherence tomography (OCT) scanner, in which case the imageprocessing techniques described herein are applicable to the tomographicimages acquired by the OCT scanner. As a further alternative, the ocularimaging system 520 may be a combined SLO-OCT scanner, in which case theimage processing techniques described herein are applicable to both theSLO retinal scans and the OCT scans acquired by the combined SLO-OCTscanner.

The receiver module 110 is configured to receive image data defining theimage acquired by the ocular imaging system 520 by any suitable meansknown to those versed in the art. For example, the receiver module 110may receive the image data from the ocular imaging system 520 via adirect communication link (which may be provided by any suitable wiredor wireless connection, e.g. a Universal Serial Bus (USB) or aBluetooth™ connection), or an indirect communication link (which may beprovided by a network comprising a Local Area Network (LAN), a Wide AreaNetwork (WAN) and/or the Internet). Furthermore, the image data may bereceived by the receiver module 110 receiving (e.g. by reading from astorage medium such as a CD or hard disk, or receiving via a networksuch as the Internet) such image data after it has been acquired by theocular imaging system.

Furthermore, the image data may be received by the receiver module 110(and may furthermore subsequently be processed to search for a regionindicative of a pathology in an image of a portion of an eye, asdescribed below) as this image data is being generated by the ocularimaging system, i.e. the image data may be acquired “on the fly”,without waiting for the ocular imaging system 520 to finish generatingall of the image data that forms the image of the portion of the retina.However, in the present example embodiment, and for the purposes of thisdescription, the receiver module 110 is configured to receive all of theimage data defining the image of the portion of the eye before thesearch module 120 begins to process this image data.

In embodiments like the present illustrated embodiment, where theapparatus 100 comprises a display control signal generator 140, thedisplay control signal generator 140 may be arranged to generate displaycontrol signals for controlling a display device (as shown at 215 inFIG. 2), such as an LCD screen or other type of visual display unit, todisplay both the location of the region indicative of the pathology inthe image of the portion of the eye, and a representation of thereceived measurement data.

FIG. 2 is a schematic illustration of a programmable signal processinghardware 200, which may, as in the present example embodiment, beconfigured to function as the apparatus 100 of FIG. 1. The programmablesignal processing hardware 200 comprises a communication interface (I/F)210 for receiving the image data described above, generating theinstruction for the eye measurement apparatus 300 to perform themeasurement on the portion of the eye to generate the measurement data,receiving the measurement data from the eye measurement apparatus 300,and, optionally, for outputting display control signals for controllingthe display device 215 to display both the image of the portion of theeye and a representation of the measurement data. The signal processingapparatus 200 further comprises a processor (e.g. a Central ProcessingUnit, CPU, or Graphics Processing Unit, GPU) 220, a working memory 230(e.g. a random access memory) and an instruction store 240 storing acomputer program comprising the computer-readable instructions which,when executed by the processor 220, cause the processor 220 to performvarious functions including those of the search module 120, instructiongenerating module 130 and, optionally, the display control signalgenerator 140 described above. The instruction store 240 may comprise aROM (e.g. in the form of an electrically-erasable programmable read-onlymemory (EEPROM) or flash memory) which is pre-loaded with thecomputer-readable instructions. Alternatively, the instruction store 240may comprise a RAM or similar type of memory, and the computer-readableinstructions of the computer program can be input thereto from acomputer program product, such as a non-transitory, computer-readablestorage medium 250 in the form of a CD-ROM, DVD-ROM, etc. or acomputer-readable signal 260 carrying the computer-readableinstructions. In any case, the computer program, when executed by theprocessor, causes the processor to execute at least one of the methodsof searching for a region indicative of a pathology, or the presence ofa pathology, in an image of a portion of an eye acquired by an ocularimaging system described herein. It should be noted, however, that theapparatus 100 may alternatively be implemented in non-programmablehardware, such as an application-specific integrated circuit (ASIC).

In the present example embodiment, a combination 270 of the hardwarecomponents shown in FIG. 2, comprising the processor 220, the workingmemory 230 and the instruction store 240, is configured to performfunctions of the search module 120 and instruction generating module130, which functions will now be described in detail below. Inembodiments like the present illustrated embodiment, where the apparatus100 comprises a display control signal generator 140, the functionalityof this optional component also be provided by the combination 270 ofthe hardware components, together with the communication I/F 210.

As will become more apparent from the following description of theoperations performed by the apparatus 100 of the present exampleembodiment, the apparatus 100 automatically processes image datadefining an image of the portion of an eye acquired by an ocular imagingsystem 520 to find, and record the location in the image of, each regionthat is indicative of pathology, and generates a correspondinginstruction for an eye measurement apparatus 300 to perform ameasurement on the portion of the eye to generate measurement data,using a reference point based on the recorded location of the respectiveregion for setting a respective location of the measurement on theportion of the eye. The apparatus 100 thus acquires both the location ofeach image region suspected to contain a pathology, and additionalmeasurement data related to the respective region. These different butcomplementary kinds of information that are automatically acquired bythe apparatus 100 can be presented together to the medical practitionerfor inspection, and may thus allow any pathology-containing regions ofthe received ocular image to be identified more quickly and with agreater degree of confidence than by inspecting the ocular image alone.For example, in example embodiments where the recorded location of theregion in the image and the representation of the received measurementdata are both displayed on the display device 215 (preferably overlaidon one another), a viewer of the display device 215 may be able toeasily recognize whether, and where, such region is to be found in theimage. It may not be as easy to do so from viewing either the recordedlocation of the region in the image, or the representation of thereceived measurement data, when either type of information is displayedalone on the display device 215. Similar advantages may occur inalternative example embodiments, where the recorded location of theregion in the image and the received measurement data are processedautomatically to identify regions of interest.

FIG. 3 is a flow diagram illustrating a process by which the apparatus100 of FIG. 1 searches for a region that is indicative of a pathology inan image of a portion of an eye acquired by an ocular imaging system520, in accordance with the first example embodiment herein.

In process S10 of FIG. 3, the receiver module 110 receives image data ofa first imaging modality, which has been acquired by the ocular imagingsystem 520. The imaging modality of the ocular imaging system may, forexample, take one of the many different forms known to those versed inthe art, including OCT, colour fundus photography, fluoresceinangiography (FA), indocyanine green angiography (ICG) andautofluoresence (AF), among others. The received image data defines theimage of a portion of the eye (in this example embodiment, the retina)acquired by the ocular imaging system 520. FIG. 4(a) is a schematicillustration of an image 400 of the portion of the retina defined by thereceived image data that has been acquired by an ocular imaging system520.

In process S12 of FIG. 3, the search module 120 searches for the region(410 in FIG. 4(a)) indicative of a single type of pathology in the image400 by processing the received image data using a learning algorithmwhich has been trained on image data defining images of the retina ofhealthy eyes, and image data defining images of the retina of unhealthyeyes each having at least one region that is indicative of thepathology. The location in the image 400 of each region 410 found in thesearch is recorded by the search module 120, for example in the workingmemory 230 in case the apparatus 100 is implemented in the programmablesignal processing hardware 200 of FIG. 2.

FIG. 5 is a schematic representation illustrating how a learningalgorithm may be trained on image data 502 defining images of healthyeyes, and image data 501 defining images of unhealthy eyes. The learningalgorithm 530 is configured to learn from, and make predictions basedon, input data by building a model 540 from an example training set 500of input data, comprising the image data 502 defining images of theretina of healthy eyes, and the image data 501 defining images of theretina of unhealthy eyes. By way of example, image data 501 definesimages of the portion of unhealthy eyes, each of which has a regionindicative of severe diabetic retinopathy. The images defined by theimage data 501 in the example training set 500 may be collected byacquiring images of the retinas of multiple subjects. More generally,each image is of the same portion of the eye (or of substantially thesame portion of the eye 510 or of a part of the eye containing the sameportion) as the image defined by the received image data. Furthermore,each image defined by the image data in the example training set 500 isacquired by the ocular imaging system 520 or by the same type of ocularimaging system and operating in the same imaging modality. By way ofexample, image data 501 defines images of the portion of unhealthy eyes,each of which has a region indicative of severe diabetic retinopathy.

In embodiments where the learning algorithm 530 is a supervised learningalgorithm (such as a neural network, a support vector machine or anevolutionary algorithm, for example), each example image in the exampletraining set 500 is a pair consisting of input image data defining animage of the portion of the eye and a desired output value indicatingwhether the image is of a portion of a “healthy” or “unhealthy” eye. Thesupervised learning algorithm 530 analyses the image data in the exampletraining set 500 and produces a model 540, which can be used to classifynew unseen image data defining an image of the portion of the eye as“healthy” or “unhealthy”. As the learning algorithm 530 is trained onimage data 501 defining images of unhealthy eyes having a single type ofpathology only, the model 540 cannot distinguish between pathologies. Itcan only determine whether a region indicative of the pathology, forexample, severe diabetic retinopathy, is present or not.

In process S12 of FIG. 3, the search module 120 may, in a modificationof the present example embodiment, search for the region 410 in theimage by searching for a region 410 in the image that is indicative notof a single type of pathology, but of one of a plurality of differenttypes of pathology, by processing the received image data using thelearning algorithm. Therefore, in the modification of the presentexample embodiment, the learning algorithm may be trained on image datadefining images of the portion of unhealthy eyes each having arespective one of the different types of pathology.

FIG. 6 is a schematic illustrating how the modified learning algorithm630 may be trained on image data 604 defining images of the retina ofhealthy eyes, and image data 601, 602, 603 defining images of the retinaof unhealthy eyes, each having at least one region that is indicative ofa respective one of a plurality of different types of pathology. By wayof example, image data 601 defines images of the portion of unhealthyeyes which have glaucoma, image data 602 defines images of the portionof unhealthy eyes which have moderate diabetic retinopathy, and imagedata 603 defines images of the portion of unhealthy eyes which havesevere diabetic retinopathy.

The learning algorithm 630 is configured to learn from, and makepredictions based on, input data by building a model 640 from an exampletraining set 600 of input data, comprising the image data 604 definingimages of the retina of healthy eyes, and the image data 601, 602, 603defining images of the retina of unhealthy eyes. The images defined bythe image data 601 to 604 in the example training set 600 may becollected by acquiring images of the retinas of multiple subjects. Moregenerally, each image is of the same portion of the eye (or ofsubstantially the same portion of the eye 510 or of a part of the eyecontaining the same portion) as the image defined by the received imagedata. Furthermore, each image defined by the image data 610 to 604 inthe example training set 600 is acquired by the ocular imaging system520 or by the same type of ocular imaging system and operating in thesame imaging modality.

The learning algorithm 630 may be a supervised learning algorithm. Thus,each example image of the portion of the eye 510 in the example trainingset 600 is associated with an indicator indicating whether that image isof a portion of a “healthy” or an “unhealthy” eye and, in cases wherethe image is of a portion of a “unhealthy” eye, also a second indicatorindicating which one of the plurality of pathologies is present in theimage. The supervised learning algorithm analyses the image data in theexample training set 600 and produces a model 640, which can be used toclassify new (previously unseen) image data defining an image of theportion of the eye as one of, for example: “healthy”;“unhealthy—glaucoma”; “unhealthy—moderate diabetic retinopathy”; and“unhealthy—severe diabetic retinopathy”.

In will be evident to one skilled in the art that the apparatus 100 maybe adapted to classify additional pathologies by expanding the trainingset 600 to include, for each of the additional pathologies, image datadefining images of the retina (or other portion) of unhealthy eyeshaving that pathology, and associated indicators as described above. Forexample, the training set 600 may be expanded to include image datadefining images of the portion of unhealthy eyes having a tumour and/orimage data defining images of the portion of unhealthy eyes havingoedema. Furthermore, any of the image data 601, 602, 603 defining imagesof the portion of unhealthy eyes may be removed or replaced with imagedata defining images of the portion of unhealthy eyes having a differentpathology. A revised version of the model 640 may then be produced basedon the modified training set 600.

The supervised learning algorithm 630 may, as in the present exampleembodiment, be a neural network. Neural networks automatically generateidentifying characteristics by processing the input data, such as theimage data in the example training set 600, without any prior knowledge.

The neural network may, as in the present example embodiment, be aconvolutional neural network. Convolutional neural networks areparticularly suitable to image and video recognition tasks.

As illustrated in FIG. 7, in general, a convolution neural networkconsists of an input layer and an output layer, as well as multiplehidden layers. Each of the layers is composed of a plurality ofartificial neurons (labelled A to F in FIG. 7), and each layer mayperform different kinds of transformations on their inputs. Eachartificial neuron may be connected to multiple artificial neurons inadjacent layers. The output of each artificial neuron is computed bysome non-linear function of the sum of its inputs. Artificial neuronsand the connections therebetween typically have respective weights (WAD,WAE, etc. in FIG. 7) which determined the strength of the signal at agiven connection. These weights are adjusted as learning proceeds,thereby adjusting the output of the convolutional neural network.Signals travel from the first layer (the input layer), to the last layer(the output layer), and may traverse the layers multiple times.

The output of the neural network may be viewed as a probability of theinput image data containing identifying characteristics of the pathologyand the classification may, as in the present example embodiment,comprise determining whether the output of the trained model 640 exceedsa predetermined threshold. The predetermined threshold may represent anacceptably low probability that the input image data containsidentifying characteristics of a pathology and, therefore, a highprobability that the eye of the subject is healthy.

In the case where the learning algorithm is a neural network, as in thepresent example embodiment, the search module 120 may be configured, aspart of process S12 of FIG. 3, to search for the region 410 indicativeof the pathology for in the image by deconstructing the neural network.

When image data defining an image 410 of the portion of the eye is inputto the trained model 640, the trained model 640 classifies this image as“healthy”, or as “unhealthy” and having a particular pathology (that is,the model 640 determines whether the input image data containsidentifying characteristics of a pathology or not). Conventionalconvolutional neural networks do not output an explanation of thisclassification. Accordingly, the convolutional neural network can bedeconstructed, that is, processed in order to determine which inputvariables of the input image data (that is, pixels of the image 400)were relevant to the output of the neural network. The input variablesmost relevant to the classification as “unhealthy” and having a certainpathology of the image 400 defined by the received image data correspondto the region 410 (or regions) that are indicative of the pathology.

In case the trained model 640 classified the image 400 defined by thereceived image data as “healthy” (that is, not containing any regionsindicative of a pathology), the search module 120 may, as in the presentexample embodiment, be configured not to deconstruct the neural network.

In embodiments like the present illustrated embodiment, where the neuralnetwork is a convolutional neural network, the search module 120 may, asin the present example embodiment, be configured to deconstruct theconvolutional neural network by performing the processes illustrated inthe flow diagram of FIG. 8.

In S122 of FIG. 8, the search module 120 performs, for each of aplurality of different sections of the image 400, a process of maskingthe section of the image to generate a masked image.

In S124 of FIG. 8, the search module 120 performs, for each of aplurality of different sections of the image 400, a process of searchingfor the region in the masked image by processing image data defining themasked image using the learning algorithm.

In S126 of FIG. 8, the search module 120 performs, for each of aplurality of different sections of the image 400, a process ofdetermining a difference between a first result which is a result of thesearch performed using the image data defining the masked image, and asecond result which is a result of a search performed using the receivedimage data (that is, the image data as received, in which the section ofthe image is not masked).

In S128 of FIG. 8, the search module 120 determines, as the location ofthe region 410 in the image 400, a location of a section for which thedetermined difference is largest.

Alternatively, where the neural network is a convolutional neuralnetwork, the search module 120 may be configured to deconstruct theconvolutional neural network by:

-   -   (i) determining a relevance of each input variable of the neural        network to an output of the neural network by applying a Taylor        decomposition to each layer of the neural network, from a top        layer of the neural network to an input layer of the neural        network; and    -   (ii) determining the location to be recorded based on at least        one section of the received image data corresponding to the most        relevant input variables of the neural network.

Taylor decomposition is a method of explaining individual neural networkpredictions by decomposing the output of the neural network on its inputvariables. This method views the image 400 as a set of pixel valuesx={x_(p)}, where p denotes a particular pixel. A function ƒ(x)quantifies the presence of a certain type of object, here a region 410indicative of a pathology, in the image 400. A function value ƒ(x)=0indicates an absence of it. On the other hand, a function value ƒ(x)>0expresses its presence with a certain degree of certainty, or in acertain amount. The Taylor decomposition method assigns each pixel p inthe image a relevance score R_(p)(x), that indicates for an image x towhat extent the pixel p contributes to explaining the classificationdecision ƒ(x). The relevance of each pixel can be stored in a heat mapdenoted by R(x)={R_(p)(x)} of same dimensions as x and can be visualizedas an image. Accordingly, the location of the region can be determinedas the location of the most relevant pixels in the heat map.

As a further alternative, where the neural network is a convolutionalneural network, the search module 120 may be configured to deconstructthe convolutional neural network by determining a deconvolution of theconvolutional neural network. In particular, the convolution neuralnetwork may be deconstructed by training a multi-layer deconvolutionnetwork which takes, as an input, the output of the convolutional neuralnetwork and provides, as an output, a probability map of the same sizeas the image input to the convolutional neural network which indicatesthe probability of each pixel belonging to one of the predefinedclasses. Accordingly, the location of the region may be determined asthe location of the pixels having the highest probability of belongingto one of the predetermined classes (that is, the highest probability ofhaving one of the plurality of different types of pathology).

Therefore, the search module 120 may, as in the present exampleembodiment, be configured to search for the region 410 indicative of apathology in the image by identifying, as the region 410 indicative of apathology, the pixels of the image 400 defined by the received imagedata that are most relevant to the output of the neural network.

Referring again to FIG. 3, in process S13, the search module 120determines whether a region in the image that is indicative of thepathology has been found as a result of the search performed in processS12. If a region in the image that is indicative of the pathology hasbeen found as a result of the search performed in process S12, theprocess proceeds to S14, otherwise the process ends.

In process S14 of FIG. 3, the instruction generating module 130determines the location in the image of the region 410 which has beenfound by the search module 120. The location may, for example, berecorded by the search module 120 in the working memory 230 of thehardware configuration 200 illustrated in FIG. 2, and read from theworking memory by the instruction generating module 130 in S14.

In embodiments like the present example embodiment, where processresults of the apparatus 100 are displayed on a display device 215 asherein described, the determined location may, as illustrated in FIG.4(a), serve to center or otherwise position an overlay graphicalelement, such as the boundary box 440 illustrated in FIG. 4(b), over theregion 410 of the image 400 found to contain the pathology.

In process S16 of FIG. 3, the instruction generating module 130generates an instruction for an eye measurement apparatus 300 to performa measurement on the portion of the eye to generate measurement data,using a reference point based on the determined location for setting alocation of the measurement on the portion of the eye. The instructiongenerated in process S16 of FIG. 3 may be provided to the eyemeasurement apparatus 300 by any suitable means known to those versed inthe art.

In embodiments like the present illustrated embodiment, the determinedlocation is a location in the image 400 of a group of one or more pixelsconstituting the region 410. In order to determine a reference pointthat can be used to set a location of the measurement to be performed bythe eye measurement apparatus 300 on the portion of the eye, thelocation recorded by the search module 120 in S12 can be transformedinto a corresponding set of one or more control parameters for steeringthe eye measurement apparatus 300 to perform its measurement atsubstantially the same location in/on the eye as that imaged by theocular imaging system 520. This can be done in one of a number ofdifferent ways.

For example, the instruction generating module 130 may use a mappingbetween pixel coordinate values from images acquired by the SLO andcorresponding values of the control parameters, which may be provided inthe form of a look-up table or a function defined by a set ofparameters, for example. The mapping may be determined by calibration,using techniques known to those skilled in the art.

In process S18 of FIG. 3, the receiver module 110 receives themeasurement data from the eye measurement apparatus 300, and the processthen ends.

The eye measurement apparatus 300 may be of any kind of apparatus thatallows a supplementary measurement of the subject's eye to be made,which could be used to verify that the region found by the search module120 does (or is highly likely to) contain an imaged pathology. The eyemeasurement apparatus 300 may, for example, measure a functionalresponse of the subject's eye to stimulation by light in response to aninstruction generated by the instruction generating module 130, andcommunicate data representing the measured functional response to thereceiver module 110.

Alternatively, the eye measurement apparatus 300 may, as in the presentembodiment, be configured to perform, as the measurement on the retina(or other selected portion of the eye, as noted above), an image captureprocess of a second imaging modality to image a region of the retina,using the aforementioned reference point for setting, as the location ofthe measurement, a location of the region of the retina to be imaged inthe image capture process, wherein the second imaging modality isdifferent to the first imaging modality (i.e. SLO in the present exampleembodiment). Thus, in the present example embodiment, where a region inthe image that is indicative of the pathology is found in S12 of FIG. 2,the instruction generating module 120 generates in S16 of FIG. 3, as theinstruction for the eye measurement apparatus 300 to perform themeasurement on the portion of the eye, an instruction for the eyemeasurement apparatus 300 to perform the image capture process using thereference point for setting, as the location of the measurement, alocation of a region in the portion of the eye to be imaged in the imagecapture process. The receiver module 110 then receives in process S18 ofFIG. 3, as the measurement data, image data of the second imagingmodality acquired by the eye measurement apparatus 300. It is noted thata single, multi-modal ocular imaging system capable of acquiring ocularimages of two or more different imaging modalities (e.g. a combinedSLO-OCT imaging system) may provide the functionality of the ocularimaging system 520 and the eye measurement apparatus 300. The ocularimaging system 520 and the eye measurement apparatus may alternativelybe provided as separate components.

The mapping discussed above may rely on the subject's eye beingpositioned and oriented in relation to the eye measuring apparatus in apredetermined way. While this may be a good assumption in any practicalapplications, it may not hold true in all cases. Where this assumptiondoes not hold, it may be preferable to define the location of the regionfound by the search module 120 in relation to an anatomical feature(e.g. the fovea) that is recognisable in both the image acquired by theocular imaging system 520 and the image of a different modality that isacquired by the eye measurement apparatus 300 of the present exampleembodiment. The location of the region relative to the position of thefovea in the image 400 may then be transformed into a correspondinglocation relative to the position of the fovea in an image acquired bythe eye measurement apparatus 300.

The reference point may be used to set the location of the measurementon the portion of the eye in any suitable way. For example, the eyemeasurement apparatus 300 may be controlled to image (or more generallyperform any other kind of measurement on) a region of a predeterminedsize that is centred on a location on the subject's retina correspondingto the reference point.

In the modification of the first example embodiment described above,where the search module 120 searches for a region 410 in the image thatis indicative of one of a plurality of different types of pathology byprocessing the received image data using the learning algorithm, theinstruction generating module 130 performs, in response to a region 410in the image 400 that is indicative of one of the plurality of differenttypes of pathology being found by the search module 120, a process ofselecting, for the one of the plurality of different types of pathologyand as the second imaging modality, a respective one of a plurality ofdifferent types of imaging modality which is to be used to perform theimage capture process on the retina of the eye. In the modification ofthe first embodiment, the instruction generating module 130 may, forexample, select the respective one of a plurality of different types ofimaging modality, which is to be used to perform the image captureprocess on the subject's retina, by consulting a look-up table (LUT) inwhich an indicator of each of the plurality of different types ofpathology is stored in association with a respective indicator of one ofthe plurality of different types of imaging modality, and using the oneof the plurality of different types of pathology found by the searchmodule 120 as a search key.

For example, in a case where the one of the plurality of different typesof pathology is glaucoma, the instruction generating module 130 maygenerate an instruction for the eye measurement apparatus 300 toperform, as the image capture process of the second imaging modality, anOCT scan of the region on the retina of the subject's eye. The receivermodule 110 may then receive in S18 of FIG. 3, as the measurement data,image data of the OCT scan, which the apparatus 100 can process toobtain complementary data that is useful for detecting the glaucoma (andoptionally estimating its severity), namely a measurement of a retinalnerve fibre layer and/or an optic nerve head of the eye.

As a further example, in a case where the one of the plurality ofdifferent types of pathology is severe diabetic retinopathy, theinstruction generating module 130 may generate an instruction for theeye measurement apparatus 300 to perform, as the image capture processof the second imaging modality, an OCT scan of a region of a retina ofthe eye. The receiver module 110 may then receive in S18 of FIG. 3, asthe measurement data, image data of the OCT scan, which the apparatus100 can process to obtain complementary data that is useful fordetecting the severe diabetic retinopathy (and optionally estimating itsseverity), namely measurements of macular thickness.

As another example, in a case where the one of the plurality ofdifferent types of pathology is a tumour, the instruction generatingmodule 130 may generate an instruction for the eye measurement apparatus300 to perform, as the image capture process of the second imagingmodality, a high-density OCT B-scan of the region in the retina of theeye. The receiver module 110 may then receive in S18 of FIG. 3, as themeasurement data, image data of the high-density OCT B-scan, which canbe useful for detecting the tumour (and optionally estimating its size).

As a yet further example, in a case where the one of the plurality ofdifferent types of pathology is drusen, the instruction generatingmodule 130 may generate an instruction for the eye measurement apparatus300 to perform, as the image capture process of the second imagingmodality, an OCT B-scan of the region in the retina of the eye. Thereceiver module 110 may then receive in S18 of FIG. 3, as themeasurement data, image data of the OCT B-scan, which can be useful fordetecting the presence of drusen or estimating their size and/or number.This data can be valuable in diagnosing age-related macular degenerationat an early stage, for example.

Furthermore, in a case where the one of the plurality of different typesof pathology is oedema or atrophy, the instruction generating module 130may generate an instruction for the eye measurement apparatus 300 toperform, as the image capture process of the second imaging modality, anOCT scan of the region in the retina of the eye. The receiver module 110may then receive in S18 of FIG. 3, as the measurement data, image dataof the OCT scan, which can be useful for detecting the oedema (oratrophy, as the case may be), and optionally estimating its severity.

In the modification of the first example embodiment described above, thesearch module 120 searches for a region 410 in the image that isindicative of one of a plurality of different types of pathology byprocessing the received image data using the learning algorithm, and theinstruction generating module 130 performs, in response to a region 410in the image 400 that is indicative of one of the plurality of differenttypes of pathology being found by the search module 120, a process ofselecting, for the one of the plurality of different types of pathologyand as the second imaging modality, a respective one of a plurality ofdifferent types of imaging modality which is to be used to perform theimage capture process on the retina of the eye. However, as noted above,the eye measurement apparatus 300 need not be configured to acquire animage of the retina (or, more generally, any other selected portion ofthe eye) but may instead perform a different kind of measurement on theeye. Moreover, the eye measurement apparatus 300 may be multi-modal inthe sense of being operable in a selected one of a plurality ofdifferent measurement modalities. In such variants, the instructiongenerating module 130 may perform, in response to a region 410 in theimage 400 that is indicative of one of the plurality of different typesof pathology being found by the search module 120, processes of:selecting, for the one of the plurality of different types of pathology,a respective one of a plurality of different types of measurementmodality for a measurement to be performed on the eye; and generating,as the instruction for the eye measurement apparatus 300 to perform themeasurement on the portion of the eye, an instruction for an eyemeasurement apparatus 300 of the selected measurement modality toperform the measurement on the portion of the eye, using the referencepoint for setting the location of the measurement on the portion of theeye.

In a second modification of the first example embodiment, the eyemeasurement apparatus 300 may be an ocular imaging apparatus of the sameimaging modality as the ocular imaging system 520 which, similar to thefirst embodiment, does not distinguish between different types ofpathology. In this second modification, the receiver module 110 isconfigured to receive image data representing a result of imaging theportion of the eye using a first value of an imaging parameter, theimaging parameter being an imaging resolution, an aperture size orwavelength used in the imaging. The instruction generating module 130 isconfigured to perform, in response to a region 410 in the image 400 thatis indicative of the pathology being found by the search module 120, aprocess of generating, as the instruction for the eye measurementapparatus 300 to perform the measurement on the portion of the eye, aninstruction for the eye measurement apparatus 300 to perform an imagecapture process using a second value of the imaging parameter to image aregion in the portion of the eye, again using the reference point forsetting, as the location of the measurement, a location of a region inthe portion of the eye to be imaged in the image capture process,wherein the second value of the imaging parameter is different from thefirst value of the imaging parameter. The receiver module 110 isconfigured to receive from the eye measurement apparatus 300, as themeasurement data, the image data representing the result of imaging theregion in the portion of the eye using the second value of the imagingparameter and the received image data and the received measurement dataare of the same imaging modality.

For example, in the second modification, the ocular imaging system 520may be configured to acquire image data defining the image by imagingthe portion of the eye at a first imaging resolution, and theinstruction generating module 130 may be configured to generate, inresponse to a region in the image that is indicative of pathology beingfound by the search module 120, an instruction for the eye measurementapparatus 300 to perform an image capture process at a second, higherimaging resolution to image the region in more detail.

Embodiment 2

FIG. 9 is a schematic illustration of an apparatus 800 for searching forthe presence of a pathology in an image of a portion of an eye acquiredby an ocular imaging system (520 in FIG. 5), in accordance with a secondexample embodiment herein. The apparatus 800 of the second exampleembodiment differs from the apparatus 100 of FIG. 1 in that theapparatus of FIG. 9 is not required to determine the location of aregion indicative of a pathology in the received image 400.

The apparatus 800 comprises a receiver module 810configured to receiveimage data defining an image produced by a ocular imaging system 520,and a search module 820 configured to search for the presence of atleast one of a plurality of different types of pathology in the image.The apparatus 800 further comprises an instruction generating module 830configured to perform, in response to at least one of the plurality ofdifferent types of pathology being found to be present in the image bythe search module 820, processes of: selecting, for each of at least onetype of pathology found to be present in the image, a respective one ofa plurality of different types of measurement modality which is to beused to perform a measurement on the portion of the eye; and generating,for each of the at least one type of pathology found to be present inthe image, a respective instruction for an eye measurement apparatus ofthe respective selected measurement modality to perform the measurementon the portion of the eye. The receiver module 810 is further configuredto receive measurement data of the measurement performed by the eyemeasurement apparatus of each selected measurement modality. Optionally,the apparatus 800 may, as in the present illustrated embodiment comprisea display control signal generator 840 (this optional component beingshown by the dashed lines in FIG. 9).

The apparatus 800 may be configured to search for the presence of anypathology, (including, for example, glaucoma, moderate diabeticretinopathy, severe diabetic retinopathy, tumour, drusen, oedema andatrophy), as discussed above in relation to the first embodiment.

In the present example embodiment, the receiver module 810 is configuredto receive image data defining an image of a portion of a retina of theeye, which has been generated by the ocular imaging system 520. However,in other example embodiments, the received image data may define animage of a portion of the eye other than the retina, for example aportion of the anterior segment of the eye, or a portion of theposterior segment of the eye.

The ocular imaging system 520 may, as in the present example embodiment,be a scanning laser ophthalmoscope. Alternatively, the ocular imagingsystem may be any ocular imaging system described above in relation tothe first embodiment.

The receiver module 810 may be configured to receive image data definingthe image acquired by the ocular imaging system 520 by any of the meansdiscussed above in relation to the first embodiment.

As will become more apparent from the following description of theoperations performed by the apparatus 800 of the example embodiment, theapparatus 800 automatically processes image data defining an image ofthe portion of an eye acquired by an ocular imaging system 520 to searchfor the presence of at least one of a plurality of different types ofpathology in the image and, when at least one of the plurality ofdifferent types of pathology is found to be present in the image,selects for each of at least one type of pathology found to be presentin the image a respective one of a plurality of different types ofmeasurement modality which is to be used to perform a measurement on theportion of the eye. A respective instruction for an eye measurementapparatus of the respective selected measurement modality to perform themeasurement on the portion of the eye is then generated for each of theat least one type of pathology found to be present in the image. Theapparatus 800 thus acquires both the image suspected to contain aparticular type of pathology, and additional measurement data of ameasurement modality that is related to the suspected type of pathology.These different but complementary kinds of information that areautomatically acquired by the apparatus 800 may allow anypathology-containing ocular image to be identified quickly and with ahigh degree of confidence.

The apparatus 800 of FIG. 9 may be implemented by a signal processinghardware configuration, such as that shown in the FIG. 2, or by anyother suitable means.

FIG. 10 is a flow diagram illustrating a process by which the apparatus800 of FIG. 9 searches for the presence of a pathology in an image of aportion of an eye acquired by an ocular imaging system 520, inaccordance with the second example embodiment herein.

In process S20 of FIG. 10, the receiver module 810 receives image datadefining the image of the portion of the eye acquired by the SLO (as anexample of the ocular imaging system 520).

In process S22 of FIG. 10, the search module 820 searches for thepresence of at least one of a plurality of different types of pathologyin the image by processing the received image data using a learningalgorithm trained on image data defining images of healthy eyes, andimages of unhealthy eyes each having a respective one of the differenttypes of pathology. The learning algorithm may be trained as discussedabove in relation to FIG. 6 in order to classify input image datadefining an image of the portion of an eye as “healthy” or as“unhealthy” and having a particular pathology.

The apparatus 800 is not required to record a location of a regionindicative of pathology in the image. Therefore, the apparatus 800 may,as in the present example embodiment, not process the learning algorithmin order to determine which input variables of the input image data(that is, pixels of the image defined by the received image data) wererelevant to the output (that is, the finding that one of a plurality ofdifferent types of pathology is present). Alternatively, in otherembodiments, such processing may optionally be carried out by theapparatus 800 as part of S22 of FIG. 10 in order to identify a referencepoint.

In process S23, the search module 820 determines whether at least one ofthe different types of pathology has been found to be present in theimage, as a result of the search performed in process S22. If at leastone of the different types of pathology has been found to be present inthe image as a result of the search performed in process S22, theprocess proceeds to S24, otherwise the process ends.

In process S24 of FIG. 10, in response to at least one of the pluralityof different types of pathology being found to be present in the imageby the search module 820, the instruction generating module 830 performsthe process of selecting, for each of at least one type of pathologyfound to be present in the image, a respective one of a plurality ofdifferent types of measurement modality which is to be used to perform ameasurement on the portion of the eye.

The instruction generating module 830 may select a respective one of aplurality of different types of measurement modality by any of the meansdescribed above in relation to the apparatus 100 of FIG. 1.

In process S26 of FIG. 10, in response to at least one of the pluralityof different types of pathology being found to be present in the imageby the search module 820, the instruction generating module 830 performsthe process of generating, for each of the at least one type ofpathology found to be present in the image, a respective instruction foran eye measurement apparatus of the respective selected measurementmodality to perform the measurement on the portion of the eye.

The instruction may be generated substantially as discussed above inrelation to apparatus 100 of FIG. 1. However, in embodiments like thepresent illustrated embodiment where the apparatus 800 does not searchfor a region indicative of a pathology, the instruction generatingmodule 830 need not use a reference point based on a recorded locationof a region indicative of pathology for setting a location of themeasurement on the portion of the eye. As part of process S26 of FIG.10, the instruction generating module 130 may, as in the present exampleembodiment, generate, for each of the at least one type of pathologyfound to be present in the image, a respective instruction for an eyemeasurement apparatus of the respective selected measurement modality toperform the measurement on the same portion of the eye as imaged by theocular imaging system.

In process S28 of FIG. 10 the receiver module 810 receives measurementdata of the measurement performed by the eye measurement apparatus ofeach selected measurement modality. The receiver module 810 may receivemeasurement data by any of the means discussed above in relation of thefirst embodiment.

Some of the embodiments described above are summarised in the followingexamples E1 to E52:

E1. A computer-implemented method of searching for a region (410)indicative of a pathology in an image (400) of a portion of an eyeacquired by an ocular imaging system (520), the method comprising:

-   -   receiving (S10) image data defining the image (400);    -   searching (S12) for the region (410) in the image (400) by        processing the received image data using a learning algorithm        (530; 630) trained on image data (502; 604) defining images of        the portion of healthy eyes, and image data (601, 602, 603; 501)        defining images of the portion of unhealthy eyes each having at        least one region that is indicative of the pathology; and    -   in case a region (410) in the image (400) that is indicative of        the pathology is found in the searching:        -   determining (S14) a location of the region (410) in the            image (400);        -   generating (S16) an instruction for an eye measurement            apparatus (300) to perform a measurement on the portion of            the eye to generate measurement data, using a reference            point based on the determined location for setting a            location of the measurement on the portion of the eye; and    -   receiving (S18) the measurement data from the eye measurement        apparatus (300).

E2. The computer-implemented method of E1, wherein

-   -   searching (S12) for the region (410) in the image (400)        comprises searching for a region (410) in the image (400) that        is indicative of one of a plurality of different types of        pathology by processing the received image data using the        learning algorithm (630), the learning algorithm being trained        on image data (601, 602, 603) defining images of the portion of        unhealthy eyes each having a respective one of the different        types of pathology, and    -   in case a region (410) in the image (400) that is indicative of        one of the plurality of different types of pathology is found in        the searching (S12):        -   the method further comprises selecting, for the one of the            plurality of different types of pathology, a respective one            of a plurality of different types of measurement modality            for a measurement to be performed on the eye; and        -   the method comprises generating (S16), as the instruction            for the eye measurement apparatus (300) to perform the            measurement on the portion of the eye, an instruction for an            eye measurement apparatus (300) of the selected measurement            modality to perform the measurement on the portion of the            eye, using the reference point for setting the location of            the measurement on the portion of the eye.

E3. The computer-implemented method of E1, wherein

-   -   the received image data is image data of a first imaging        modality,    -   the eye measurement apparatus (300) is configured to perform, as        the measurement on the portion of the eye, an image capture        process of a second imaging modality to image a region in the        portion of the eye, and to acquire, as the measurement data,        image data of the second imaging modality, the second imaging        modality being different than the first imaging modality, and    -   in the case that a region (410) in the image (400) that is        indicative of the pathology is found in the searching (S12), the        method comprises:        -   generating (S16), as the instruction for the eye measurement            apparatus (300) to perform the measurement on the portion of            the eye, an instruction for the eye measurement apparatus            (300) to perform the image capture process using the            reference point for setting, as the location of the            measurement, a location of a region in the portion of the            eye to be imaged in the image capture process; and        -   receiving (S18) from the eye measurement apparatus (300), as            the measurement data, image data of the second imaging            modality defining an image of the region in the portion of            the eye.

E4. The computer-implemented method of E3, wherein

-   -   searching (S12) for the region (410) in the image (400)        comprises searching for a region (410) in the image (400) that        is indicative of one of a plurality of different types of        pathology by processing the received image data using the        learning algorithm (630), the learning algorithm being trained        on the image data (601, 602, 603) defining images of the portion        of unhealthy eyes each having a respective one of the different        types of pathology, and    -   in case a region (410) in the image (400) that is indicative of        one of the plurality of different types of pathology is found in        the searching (S12), the method further comprises selecting, for        the one of the plurality of different types of pathology and as        the second imaging modality, a respective one of a plurality of        different types of imaging modality which is to be used to        perform the image capture process on the portion of the eye.

E5. The computer-implemented method of E4, wherein:

-   -   in a case where the one of the plurality of different types of        pathology is glaucoma, the method comprises generating (S16) an        instruction for the eye measurement apparatus to perform, as the        image capture process of the second imaging modality, an optical        coherence tomography, OCT, scan of the region in the portion of        the eye, and to acquire, as the measurement data, image data of        the OCT scan;    -   in a case where the one of the plurality of different types of        pathology is severe diabetic retinopathy, the method comprises        generating (S16) an instruction for the eye measurement        apparatus to perform, as the image capture process of the second        imaging modality, an optical coherence tomography, OCT, scan of        a region of a retina of the eye, and to acquire, as the        measurement data, image data of the OCT scan;    -   in a case where the one of the plurality of different types of        pathology is a tumour, the method comprises generating (S16) an        instruction for the eye measurement apparatus to perform, as the        image capture process of the second imaging modality, a        high-density optical coherence tomography, OCT, B-scan of the        region in the portion of the eye, and to acquire, as the        measurement data, image data of the high-density OCT B-scan;    -   in a case where the one of the plurality of different types of        pathology is drusen, the method comprises generating (S16) an        instruction for the eye measurement apparatus to perform, as the        image capture process of the second imaging modality, an optical        coherence tomography, OCT, B-scan of the region in the portion        of the eye, and to acquire, as the measurement data, image data        of the OCT B-scan;    -   in a case where the one of the plurality of different types of        pathology is oedema or atrophy, the method comprises generating        (S16) an instruction for the eye measurement apparatus to        perform, as the image capture process of the second imaging        modality, an optical coherence tomography, OCT, scan of the        region in the portion of the eye, and to acquire, as the        measurement data, image data of the OCT scan.

E6. The computer-implemented method of E1, wherein, in case the region(410) in the image (400) that is indicative of the pathology is found inthe searching, the method comprises generating (S16), as the instructionfor the eye measurement apparatus to perform the measurement on theportion of the eye, an instruction for the eye measurement apparatus tomeasure a functional response of the eye to light stimulation, using thereference point for setting the location of the measurement which isbased on the determined location.

E7. The computer-implemented method of E1, wherein

-   -   the received image data represents a result of imaging the        portion of the eye using a first value of an imaging parameter,        the imaging parameter being one of an imaging resolution, an        aperture size and wavelength used in the imaging,    -   the eye measurement apparatus is configured to perform, as the        measurement on the portion of the eye, an image capture process        using a second value of the imaging parameter to image a region        in the portion of the eye, and to acquire, as the measurement        data, image data representing a result of imaging the region        using the second value of the imaging parameter, wherein the        second value of the imaging parameter is different from the        first value of the imaging parameter, and the received image        data and the acquired image data are of the same imaging        modality, and    -   in the case that a region (410) in the image (400) that is        indicative of the pathology is found in the searching, the        method comprises:        -   generating (S16), as the instruction for the eye measurement            apparatus to perform the measurement on the portion of the            eye, an instruction for the eye measurement apparatus to            perform the image capture process, using the reference point            for setting, as the location of the measurement, a location            of a region in the portion of the eye to be imaged in the            image capture process; and        -   receiving (S18) from the eye measurement apparatus, as the            measurement data, the image data representing the result of            imaging the region (410) in the portion of the eye using the            second value of the imaging parameter.

E8. The computer-implemented method of any of E1 to E7, furthercomprising generating instructions for controlling a display unit (215)to display the location of the region (410) in the image (400) of theportion of the eye and a representation of the received measurementdata.

E9. The computer-implemented method of any of E1 to E8, wherein thelearning algorithm (530; 630) is a supervised learning algorithm.

E10. The computer-implemented method of E9, wherein the supervisedlearning algorithm comprises a neural network, and the region (410)indicative of the pathology is searched for in the image (400) bydeconstructing the neural network.

E11. The computer-implemented method of E10, wherein the neural networkis a convolutional neural network, and the neural network isdeconstructed by:

-   -   performing, for each of a plurality of different sections of the        image (400) that is defined by the received image data,        processes of:        -   masking (S122) the section of the image (400) to generate a            masked image;        -   searching (S124) for the region in the masked image by            processing image data defining the masked image using the            learning algorithm; and        -   determining (S126) a difference between a result of the            search performed using the image data defining the masked            image and a result of a search performed using the received            image data; and    -   determining (S128), as the location of the region (410) in the        image (400), a location of a section for which the determined        difference is largest.

E12. The computer-implemented method of E10, wherein the neural networkis a convolutional neural network, and the convolutional neural networkis deconstructed by:

-   -   determining a relevance of each input variable of the neural        network to an output of the neural network by applying a Taylor        decomposition to each layer of the neural network, from a top        layer of the neural network to an input layer of the neural        network; and    -   determining the location of the region (410) in the image (400)        based on at least one section of the received image data        corresponding to the most relevant input variables of the neural        network.

E13. The computer-implemented method of E10, wherein the neural networkis a convolutional neural network, and the convolutional neural networkis deconstructed by determining a deconvolution of the convolutionalneural network.

E14. A computer-implemented method of searching for the presence of apathology in an image (400) of a portion of an eye acquired by an ocularimaging system (520), the method comprising:

-   -   receiving (S20) image data defining the image (400);    -   searching (S22) for the presence of at least one of a plurality        of different types of pathology in the image (400) by processing        the received image data using a learning algorithm (630) trained        on image data defining images (502) of healthy eyes, and images        (601, 602, 603) of unhealthy eyes each having a respective one        of the different types of pathology; and    -   in case at least one of the plurality of different types of        pathology is found to be present in the image (400):        -   selecting (S24), for each of at least one type of pathology            found to be present in the image (400), a respective one of            a plurality of different types of measurement modality which            is to be used to perform a measurement on the portion of the            eye;        -   generating (S26), for each of the at least one type of            pathology found to be present in the image (400), a            respective instruction for an eye measurement apparatus            (300) of the respective selected measurement modality to            perform the measurement on the portion of the eye; and        -   receiving (S28) measurement data of the measurement            performed by the eye measurement apparatus (300) of each            selected measurement modality.

E15. The computer-implemented method of E14, further comprising, in caseat least one of the plurality of different types of pathology is foundto be present in the image (400):

-   -   for each of the at least one of the different types of pathology        found to be present in the image, searching for a respective        region (410) in the image (400) that is indicative of the        respective type of pathology, by processing the received image        data using the learning algorithm (630), and recording a        location of the respective region (410) in the image (400),        wherein    -   a respective instruction for the eye measurement apparatus (300)        of each selected measurement modality to perform a measurement        on the eye using a reference point for locating the measurement        which is based on the respective recorded location is generated.

E16. The computer-implemented method of E15, wherein, in case at leastone of the plurality of different types of pathology is found to bepresent in the image (400),

-   -   for each of the at least one of the different types of pathology        found to be present in the image (400):        -   a respective one of a plurality of different types of            imaging modality which is to be used to image the portion of            the eye is selected as the respective one of the plurality            of different types of measurement modality;        -   a respective instruction for an eye measurement apparatus            (300) of the selected imaging modality to image the portion            of the eye, using a reference point for locating a region of            the eye to be imaged which is based on the respective            recorded location, is generated as the respective            instruction for the eye measurement apparatus (300) of the            selected measurement modality; and        -   respective image data of the imaging performed by the eye            measurement apparatus (300) of the selected imaging modality            is received as the measurement data of the measurement            performed by the eye measurement apparatus (300).

E17. The computer-implemented method of E16, wherein:

-   -   in a case where one of the plurality of different types of        pathology found to be present in the image (400) is glaucoma,        the method comprises generating (S26), as the respective        instruction for the eye measurement apparatus (300) of the        selected imaging modality to image the portion of the eye, an        instruction for the eye measurement apparatus (300) to perform        an optical coherence tomography, OCT, scan of the region in    -   the portion of the eye, and to acquire, as the measurement data,        image data of the OCT scan;    -   in a case where one of the plurality of different types of        pathology found to be present in the image (400) is severe        diabetic retinopathy, the method comprises generating (S26), as        the respective instruction for the eye measurement apparatus        (300) of the selected imaging modality to image the portion of        the eye, an instruction for the eye measurement apparatus (300)        to perform an optical coherence tomography, OCT, scan of a        region of a retina of the eye, and to acquire, as the        measurement data, image data of the OCT scan;    -   in a case where one of the plurality of different types of        pathology found to be present in the image (400) is a tumour,        the method comprises generating (S26), as the respective        instruction for the eye measurement apparatus (300) of the        selected imaging modality to image the portion of the eye, an        instruction for the eye measurement apparatus (300) to perform a        high-density optical coherence tomography, OCT, B-scan of the        region in the portion of the eye, and to acquire, as the        measurement data, image data of the high-density OCT B-scan;    -   in a case where the one of the plurality of different types of        pathology found to be present in the image (400) is drusen, the        method comprises generating (S26), as the respective instruction        for the eye measurement apparatus (300) of the selected imaging        modality to image the portion of the eye, an instruction for the        eye measurement apparatus (300) to perform an optical coherence        tomography, OCT, B-scan of the region in the portion of the eye,        and to acquire, as the measurement data, image data of the OCT        B-scan; and    -   in a case where the one of the plurality of different types of        pathology found to be present in the image (400) is oedema or        atrophy, the method comprises generating (S26), as the        respective instruction for the eye measurement apparatus (300)        of the selected imaging modality to image the portion of the        eye, an instruction for the eye measurement apparatus (300) to        perform an optical coherence tomography, OCT, scan of the region        in the portion of the eye, and to acquire, as the measurement        data, image data of the OCT scan.

E18. The computer-implemented method of E14, wherein, in case at leastone of the plurality of different types of pathology is found to bepresent in the image (400), at least one of the instructions generatedis an instruction for an eye measurement apparatus (300) of a selectedmeasurement modality to measure a functional response of the eye tolight stimulation.

E19. The computer-implemented method of any of E15 to E17, furthercomprising generating instructions for controlling a display unit (215)to display the recorded location of the region (410) in the image (400)of the portion of the eye and a representation of the receivedmeasurement data.

E20. The computer-implemented method of any of E15 to E17, wherein thelearning algorithm (630) is a supervised learning algorithm.

E21. The computer-implemented method of E20, wherein the supervisedlearning algorithm comprises a neural network, and a region indicativeof one of the different types of pathology found to be present in theimage (400) is searched for in the image by deconstructing the neuralnetwork.

E22. The computer-implemented method of E21, wherein the neural networkis a convolutional neural network, and the neural network isdeconstructed by:

-   -   performing, for each of a plurality of different sections of the        image (400) that is defined by the received image data,        processes of:        -   masking (S122) the section of the image (400) to generate a            masked image;        -   searching (S124) for the region in the masked image by            processing image data defining the masked image using the            learning algorithm; and        -   determining (S126) a difference between a result of the            search performed using the image data defining the masked            image and a result of a search performed using the received            image data; and    -   determining (S128), as the location to be recorded, a location        of a section for which the determined difference is largest.

E23. The computer-implemented method of E21, wherein the neural networkis a convolutional neural network, and the convolutional neural networkis deconstructed by:

-   -   determining a relevance of each input variable of the neural        network to an output of the neural network by applying a Taylor        decomposition to each layer of the neural network, from a top        layer of the neural network to an input layer of the neural        network; and    -   determining the location to be recorded based on at least one        section of the received image data corresponding to the most        relevant input variables of the neural network.

E24. The computer-implemented method of E21, wherein the neural networkis a convolutional neural network, and the convolutional neural networkis deconstructed by determining a deconvolution of the convolutionalneural network.

E25. A computer program which, when executed by a computer, causes thecomputer to perform a method as set out in at least one of E1 to E24.

E26. A computer-readable storage medium (250) storing the computerprogram of E25.

E27. A signal (260) carrying the computer program of E25.

E28. An apparatus (100) for searching for a region indicative of apathology in an image (400) of a portion of an eye acquired by an ocularimaging system (520; 720), the apparatus (100) comprising:

-   -   a receiver module (110) configured to receive image data        defining the image (400);    -   a search module (120) configured to search for the region in the        image (400) by processing the received image data using a        learning algorithm (530; 630) trained on image data (502; 604)        defining images of the portion of healthy eyes, and image data        (601, 602, 603; 501) defining images of the portion of unhealthy        eyes each having at least one region that is indicative of the        pathology; and    -   an instruction generating module (130) configured to perform, in        response to a region (410) in the image (400) that is indicative        of the pathology being found by the search module (120),        processes of:        -   determining a location of the region (410) in the image            (400); and        -   generating an instruction for an eye measurement apparatus            (300) to perform a measurement on the portion of the eye to            generate measurement data, using a reference point based on            the determined location for setting a location of the            measurement on the portion of the eye,    -   wherein the receiver module (110) is further configured to        receive the measurement data from the eye measurement apparatus        (300).

E29. The apparatus (100) of E28, wherein

-   -   the search module (120) is configured to search for the region        (410) in the image (400) by searching for a region (410) in the        image (400) that is indicative of one of a plurality of        different types of pathology by processing the received image        data using the learning algorithm (630), the learning algorithm        (630) being trained on image data (601, 602, 603) defining        images of the portion of unhealthy eyes each having a respective        one of the different types of pathology,    -   the instruction generating module (130) is configured to        perform, in response to a region (410) in the image (400) that        is indicative of one of the plurality of different types of        pathology being found by the search module (120), processes of:        -   selecting, for the one of the plurality of different types            of pathology, a respective one of a plurality of different            types of measurement modality for a measurement to be            performed on the eye; and        -   generating, as the instruction for the eye measurement            apparatus (300) to perform the measurement on the portion of            the eye, an instruction for an eye measurement apparatus            (300) of the selected measurement modality to perform the            measurement on the portion of the eye, using the reference            point for setting the location of the measurement on the            portion of the eye.

E30. The apparatus (100) of E28, wherein

-   -   the receiver module (110) is configured to receive, as the image        data, image data of a first imaging modality,    -   the instruction generating module (130) is configured to        generate, in response to a region (410) in the image (400) that        is indicative of the pathology being found by the search module        (120), and as the instruction for the eye measurement apparatus        (300) to perform the measurement on the portion of the eye, an        instruction for the eye measurement apparatus (300) to perform        an image capture process of a second imaging modality to image a        region in the portion of the eye, using the reference point for        setting, as the location of the measurement, a location of a        region in the portion of the eye to be imaged in the image        capture process, the second imaging modality being different to        the first imaging modality, and    -   the receiver module (110) is configured to receive from the eye        measurement apparatus (300), as the measurement data, image data        of the second imaging modality acquired by the eye measurement        apparatus (300).

E31. The apparatus (100) of E30, wherein

-   -   the search module (120) is configured to search for a region        (410) in the image (400) that is indicative of one of a        plurality of different types of pathology by processing the        received image data using the learning algorithm (630), the        learning algorithm being trained on the image data (601, 602,        603) defining images of the portion of unhealthy eyes each        having a respective one of the different types of pathology, and    -   the instruction generating module (130) is configured to        perform, in response to a region (410) in the image (400) that        is indicative of one of the plurality of different types of        pathology being found by the search module (120), a process of        selecting, for the one of the plurality of different types of        pathology and as the second imaging modality, a respective one        of a plurality of different types of imaging modality which is        to be used to perform the image capture process on the portion        of the eye.

E32. The apparatus (100) of E31, wherein

-   -   the instruction generating module (130) is configured to        generate, in a case where the one of the plurality of different        types of pathology is glaucoma, an instruction for the eye        measurement apparatus (300) to perform, as the image capture        process of the second imaging modality, an optical coherence        tomography, OCT, scan of the region in the portion of the eye,        and the receiver module (110) is configured to receive, as the        measurement data, image data of the OCT scan;    -   the instruction generating module (130) is configured to        generate, in a case where the one of the plurality of different        types of pathology is severe diabetic retinopathy, an        instruction for the eye measurement apparatus (300) to perform,        as the image capture process of the second imaging modality, an        optical coherence tomography, OCT, scan of a region of a retina        of the eye, and the receiver module (110) is configured to        receive, as the measurement data, image data of the OCT scan;    -   the instruction generating module (130) is configured to        generate, in a case where the one of the plurality of different        types of pathology is a tumour, an instruction for the eye        measurement apparatus (300) to perform, as the image capture        process of the second imaging modality, a high-density optical        coherence tomography, OCT, B-scan of the region in the portion        of the eye, and the receiver module (110) is configured to        receive, as the measurement data, image data of the high-density        OCT B-scan;    -   the instruction generating module (130) is configured to        generate, in a case where the one of the plurality of different        types of pathology is drusen, an instruction for the eye        measurement apparatus (300) to perform, as the image capture        process of the second imaging modality, an optical coherence        tomography, OCT, B-scan of the region in the portion of the eye,        and the receiver module (110) is configured to receive, as the        measurement data, image data of the OCT B-scan; and    -   the instruction generating module (130) is configured to        generate, in a case where the one of the plurality of different        types of pathology is oedema, an instruction for the eye        measurement apparatus (300) to perform, as the image capture        process of the second imaging modality, an optical coherence        tomography, OCT, scan of the region in the portion of the eye,        and the receiver module (110) is configured to receive, as the        measurement data, image data of the OCT scan.

E33. The apparatus (100) of E28, wherein, the instruction generatingmodule (130) is configured to generate, in response to the region (410)in the image (400) that is indicative of the pathology being found bythe search module (120), and as the instruction for the eye measurementapparatus (300) to perform the measurement on the portion of the eye, aninstruction for the eye measurement apparatus (300) to measure afunctional response of the eye to light stimulation, using the referencepoint for setting the location of the measurement which is based on thedetermined location.

E34. The apparatus (100) of E28, wherein

-   -   the receiver module (110) is configured to receive image data        representing a result of imaging the portion of the eye using a        first value of an imaging parameter, the imaging parameter being        one of an imaging resolution, an aperture size and wavelength        used in the imaging,    -   the instruction generating module (130) is configured to        perform, in response to a region (410) in the image (400) that        is indicative of the pathology being found by the search module        (120), a processes of generating, as the instruction for the eye        measurement apparatus (300) to perform the measurement on the        portion of the eye, an instruction for the eye measurement        apparatus (300) to perform an image capture process using a        second value of the imaging parameter to image a region in the        portion of the eye, using the reference point for setting, as        the location of the measurement, a location of a region in the        portion of the eye to be imaged in the image capture process,        wherein the second value of the imaging parameter is different        from the first value of the imaging parameter, and    -   the receiver module (110) is configured to receive from the eye        measurement apparatus (300), as the measurement data, the image        data representing the result of imaging the region in the        portion of the eye using the second value of the imaging        parameter and the received image data and the received        measurement data are of the same imaging modality.

E35. The apparatus (100) of any of E28 to E34, wherein the instructiongenerating module (130) is further configured to generate instructionsfor controlling a display unit (215) to display the determined locationof the region (410) in the image (400) of the portion of the eye and arepresentation of the received measurement data.

E36. The apparatus (100) of any of E28 to E35, wherein the learningalgorithm (630) is a supervised learning algorithm.

E37. The apparatus (100) of E36, wherein the supervised learningalgorithm comprises a neural network, and the search module (120) isconfigured to search for the region indicative of the pathology in theimage by deconstructing the neural network.

E38. The apparatus (100) of E37, wherein the neural network is aconvolutional neural network, and the search module (120) is configuredto deconstruct the neural network by:

-   -   performing, for each of a plurality of different sections of the        image (400) that is defined by the received image data,        processes of:        -   masking the section of the image (400) to generate a masked            image;        -   searching for the region in the masked image by processing            image data defining the masked image using the learning            algorithm; and        -   determining a difference between a result of the search            performed using the image data defining the masked image and            a result of a search performed using the received image            data; and    -   determining, as the location of the region (410) in the image        (400), a location of a section for which the determined        difference is largest.

E39. The apparatus (100) of E37, wherein the neural network is aconvolutional neural network, and the search module (120) is configuredto deconstruct the convolutional neural network by:

-   -   determining a relevance of each input variable of the neural        network to an output of the neural network by applying a Taylor        decomposition to each layer of the neural network, from a top        layer of the neural network to an input layer of the neural        network; and    -   determining the location of the region (410) in the image (400)        based on at least one section of the received image data        corresponding to the most relevant input variables of the neural        network.

E40. The apparatus (100) of E37, wherein the neural network is aconvolutional neural network, and the search module (120) is configuredto deconstruct the convolutional neural network by determining adeconvolution of the convolutional neural network.

E41. An apparatus (800) for searching for the presence of a pathology inan image (400) of a portion of an eye acquired by an ocular imagingsystem (520), the apparatus (800) comprising:

-   -   a receiver module (810) configured to receive image data        defining the image (400);    -   a search module (820) configured to search for the presence of        at least one of a plurality of different types of pathology in        the image (400) by processing the received image data using a        learning algorithm (630) trained on image data defining images        (604) of healthy eyes, and images (601, 602, 603) of unhealthy        eyes each having a respective one of the different types of        pathology; and    -   an instruction generating module (830) configured to perform, in        response to at least one of the plurality of different types of        pathology being found to be present in the image (400) by the        search module (820), processes of:        -   selecting, for each of at least one type of pathology found            to be present in the image (400), a respective one of a            plurality of different types of measurement modality which            is to be used to perform a measurement on the portion of the            eye; and        -   generating, for each of the at least one type of pathology            found to be present in the image (400), a respective            instruction for an eye measurement apparatus (300) of the            respective selected measurement modality to perform the            measurement on the portion of the eye,    -   wherein the receiver module (810) is further configured to        receive measurement data of the measurement performed by the eye        measurement apparatus (300) of each selected measurement        modality.

E42. The apparatus (800) of E41, wherein the search module (820) isfurther configured to perform, in response to finding at least one ofthe plurality of different types of pathology to be present in the image(400), a process of:

-   -   for each of the at least one of the different types of pathology        found to be present in the image (400), searching for a        respective region (410) in the image (400) that is indicative of        the respective type of pathology, by processing the received        image data using the learning algorithm (630), and recording a        location of the respective region (410) in the image (400), and    -   the instruction generating module (830) is configured to        generate a respective instruction for the eye measurement        apparatus (300) of each selected measurement modality to perform        a measurement on the eye using a reference point for locating        the measurement which is based on the respective location.

E43. The apparatus (800) of E42, wherein the instruction generatingmodule (830) is configured to perform, in response to at least one ofthe plurality of different types of pathology being found to be presentin the image (400) by the search module (820), and for each of the atleast one of the different types of pathology found to be present in theimage (400), processes of:

-   -   selecting a respective one of a plurality of different types of        imaging modality which is to be used to image the portion of the        eye as the respective one of the plurality of different types of        measurement modality; and    -   generating, as the respective instruction for the eye        measurement apparatus (300) of the selected measurement        modality, a respective instruction for an eye measurement        apparatus (300) of the selected imaging modality to image the        portion of the eye, using a reference point for locating a        region of the eye to be imaged which is based on the respective        recorded location, and wherein

the receiver module (810) is configured to receive respective image dataof the imaging performed by the eye measurement apparatus (300) of theselected imaging modality as the measurement data of the measurementperformed by the eye measurement apparatus (300).

E44. The apparatus (800) of E43, wherein:

-   -   the instruction generating module (830) is configured to        generate, in a case where one of the plurality of different        types of pathology found to be present in the image (400) is        glaucoma, and as the respective instruction for the eye        measurement apparatus (300) of the selected imaging modality to        image the portion of the eye, an instruction for the eye        measurement apparatus (300) to perform an optical coherence        tomography, OCT, scan of the region in the portion of the eye,        and the receiver module (810) is configured to receive, as the        measurement data, image data of the OCT scan;    -   the instruction generating module (830) is configured to        generate, in a case where one of the plurality of different        types of pathology found to be present in the image (400) is        severe diabetic retinopathy, and as the respective instruction        for the eye measurement apparatus (300) of the selected imaging        modality to image the portion of the eye, an instruction for the        eye measurement apparatus (300) to perform an optical coherence        tomography, OCT, scan of a region of a retina of the eye, and        the receiver module (810) is configured to receive, as the        measurement data, image data of the OCT scan;    -   the instruction generating module (830) is configured to        generate, in a case where one of the plurality of different        types of pathology found to be present in the image (400) is a        tumour, and as the respective instruction for the eye        measurement apparatus (300) of the selected imaging modality to        image the portion of the eye, an instruction for the eye        measurement apparatus (300) to perform a high-density optical        coherence tomography, OCT, B-scan of the region in the portion        of the eye, and the receiver module (810) is configured to        receive, as the measurement data, image data of the high-density        OCT B-scan;    -   the instruction generating module (830) is configured to        generate, in a case where the one of the plurality of different        types of pathology found to be present in the image (400) is        drusen, and as the respective instruction for the eye        measurement apparatus (300) of the selected imaging modality to        image the portion of the eye, an instruction for the eye        measurement apparatus (300) to perform an optical coherence        tomography, OCT, B-scan of the region in the portion of the eye,        and the receiver module (810) is configured to receive, as the        measurement data, image data of the OCT B-scan; and    -   the instruction generating module (830) is configured to        generate, in a case where the one of the plurality of different        types of pathology found to be present in the image (400) is        oedema, and as the respective instruction for the eye        measurement apparatus (300) of the selected imaging modality to        image the portion of the eye, an instruction for the eye        measurement apparatus (300) to perform an optical coherence        tomography, OCT, scan of the region in the portion of the eye,        and the receiver module (810) is configured to receive, as the        measurement data, image data of the OCT scan.

E45. The apparatus (800) of E41, wherein the instruction generatingmodule (830) is configured to generate, in response to at least one ofthe plurality of different types of pathology being found to be presentin the image by the search module (820), an instruction for an eyemeasurement apparatus (300) of a selected measurement modality tomeasure a functional response of the eye to light stimulation.

E46. The apparatus (800) of any of E42 to E44, wherein the instructiongenerating module (830) is further configured to generate instructionsfor controlling a display unit (215) to display the recorded location ofthe region (410) in the image (400) of the portion of the eye and arepresentation of the received measurement data.

E47. The apparatus (800) of any of E42 to E44, wherein the learningalgorithm (630) is a supervised learning algorithm.

E48. The apparatus (800) of E47, wherein the supervised learningalgorithm comprises a neural network, and the search module (820) isconfigured to search for a region (410) indicative of one of thedifferent types of pathology found to be present in the image (400) bydeconstructing the neural network.

E49. The apparatus of E48, wherein the neural network is a convolutionalneural network, and the search module (820) is configured to deconstructthe neural network by:

-   -   performing, for each of a plurality of different sections of the        image (400) that is defined by the received image data,        processes of:        -   masking the section of the image (400) to generate a masked            image;        -   searching for the region in the masked image by processing            image data defining the masked image using the learning            algorithm; and        -   determining a difference between a result of the search            performed using the image data defining the masked image and            a result of a search performed using the received image            data; and    -   determining, as the location to be recorded, a location of a        section for which the determined difference is largest.

E50. The apparatus (800) of E48, wherein the neural network is aconvolutional neural network, and the search module (820) is configuredto deconstruct the convolutional neural network by:

-   -   determining a relevance of each input variable of the neural        network to an output of the neural network by applying a Taylor        decomposition to each layer of the neural network, from a top        layer of the neural network to an input layer of the neural        network; and    -   determining the location to be recorded based on at least one        section of the received image data corresponding to the most        relevant input variables of the neural network.

E51. The apparatus (800) of E48, wherein the neural network is aconvolutional neural network, and the search module (820) is configuredto deconstruct the convolutional neural network by determining adeconvolution of the convolutional neural network.

E52. An apparatus for searching for a region indicative of a pathologyin an image (400) of a portion of an eye acquired by an ocular imagingsystem (520; 720), the apparatus (100) comprising a processor and amemory storing computer program instructions which,

-   -   when executed by the processor, cause the processor to perform a        method as set out in at least one of E1 to E24.

In the foregoing description, example aspects are described withreference to several example embodiments. Accordingly, the specificationshould be regarded as illustrative, rather than restrictive. Similarly,the figures illustrated in the drawings, which highlight thefunctionality and advantages of the example embodiments, are presentedfor example purposes only. The architecture of the example embodimentsis sufficiently flexible and configurable, such that it may be utilized(and navigated) in ways other than those shown in the accompanyingfigures.

Software embodiments of the examples presented herein may be providedas, a computer program, or software, such as one or more programs havinginstructions or sequences of instructions, included or stored in anarticle of manufacture such as a machine-accessible or machine-readablemedium, an instruction store, or computer-readable storage device, eachof which can be non-transitory, in one example embodiment. The programor instructions on the non-transitory machine-accessible medium,machine-readable medium, instruction store, or computer-readable storagedevice, may be used to program a computer system or other electronicdevice. The machine- or computer-readable medium, instruction store, andstorage device may include, but are not limited to, floppy diskettes,optical disks, and magneto-optical disks or other types ofmedia/machine-readable medium/instruction store/storage device suitablefor storing or transmitting electronic instructions. The techniquesdescribed herein are not limited to any particular softwareconfiguration. They may find applicability in any computing orprocessing environment. The terms “computer-readable”,“machine-accessible medium”, “machine-readable medium”, “instructionstore”, and “computer-readable storage device” used herein shall includeany medium that is capable of storing, encoding, or transmittinginstructions or a sequence of instructions for execution by the machine,computer, or computer processor and that causes themachine/computer/computer processor to perform any one of the methodsdescribed herein. Furthermore, it is common in the art to speak ofsoftware, in one form or another (e.g., program, procedure, process,application, module, unit, logic, and so on), as taking an action orcausing a result. Such expressions are merely a shorthand way of statingthat the execution of the software by a processing system causes theprocessor to perform an action to produce a result.

Some embodiments may also be implemented by the preparation ofapplication-specific integrated circuits, field-programmable gatearrays, or by interconnecting an appropriate network of conventionalcomponent circuits.

Some embodiments include a computer program product. The computerprogram product may be a storage medium or media, instruction store(s),or storage device(s), having instructions stored thereon or thereinwhich can be used to control, or cause, a computer or computer processorto perform any of the procedures of the example embodiments describedherein. The storage medium/instruction store/storage device may include,by example and without limitation, an optical disc, a ROM, a RAM, anEPROM, an EEPROM, a DRAM, a VRAM, a flash memory, a flash card, amagnetic card, an optical card, nanosystems, a molecular memoryintegrated circuit, a RAID, remote data storage/archive/warehousing,and/or any other type of device suitable for storing instructions and/ordata.

Stored on any one of the computer-readable medium or media, instructionstore(s), or storage device(s), some implementations include softwarefor controlling both the hardware of the system and for enabling thesystem or microprocessor to interact with a human user or othermechanism utilizing the results of the example embodiments describedherein. Such software may include without limitation device drivers,operating systems, and user applications. Ultimately, suchcomputer-readable media or storage device(s) further include softwarefor performing example aspects of the invention, as described above.

Included in the programming and/or software of the system are softwaremodules for implementing the procedures described herein. In someexample embodiments herein, a module includes software, although inother example embodiments herein, a module includes hardware, or acombination of hardware and software.

While various example embodiments of the present invention have beendescribed above, it should be understood that they have been presentedby way of example, and not limitation. It will be apparent to personsskilled in the relevant art(s) that various changes in form and detailcan be made therein. Thus, the present invention should not be limitedby any of the above described example embodiments, but should be definedonly in accordance with the following claims and their equivalents.

Further, the purpose of the Abstract is to enable the Patent Office andthe public generally, and especially the scientists, engineers andpractitioners in the art who are not familiar with patent or legal termsor phraseology, to determine quickly from a cursory inspection thenature and essence of the technical disclosure of the application. TheAbstract is not intended to be limiting as to the scope of the exampleembodiments presented herein in any way. It is also to be understoodthat the procedures recited in the claims need not be performed in theorder presented.

While this specification contains many specific embodiment details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular embodiments described herein. Certainfeatures that are described in this specification in the context ofseparate embodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

In certain circumstances, multitasking and parallel processing may beadvantageous. Moreover, the separation of various components in theembodiments described above should not be understood as requiring suchseparation in all embodiments, and it should be understood that thedescribed program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Having now described some illustrative embodiments and embodiments, itis apparent that the foregoing is illustrative and not limiting, havingbeen presented by way of example. In particular, although many of theexamples presented herein involve specific combinations of apparatus orsoftware elements, those elements may be combined in other ways toaccomplish the same objectives. Acts, elements and features discussedonly in connection with one embodiment are not intended to be excludedfrom a similar role in other embodiments or embodiments.

The apparatus and computer programs described herein may be embodied inother specific forms without departing from the characteristics thereof.The foregoing embodiments are illustrative rather than limiting of thedescribed systems and methods. Scope of the apparatus and computerprograms described herein is thus indicated by the appended claims,rather than the foregoing description, and changes that come within themeaning and range of equivalency of the claims are embraced therein.

The invention claimed is:
 1. A computer-implemented method of searchingfor a region indicative of a pathology in an image of a portion of aneye acquired by an ocular imaging system, the method comprising:receiving image data defining the image; searching for the region in theimage by processing the received image data using a learning algorithmtrained on both first image data and second image data, the first imagedata defining images of the portion of healthy eyes, and the secondimage data defining images of the portion of unhealthy eyes each havingat least one region that is indicative of the pathology; and in case aregion in the image that is indicative of the pathology is found in thesearching: determining a location of the region in the image; generatingan instruction for an eye measurement apparatus to perform a measurementon the portion of the eye to generate measurement data, using areference point based on the determined location for setting a locationof the measurement on the portion of the eye; and receiving themeasurement data from the eye measurement apparatus, wherein searchingfor the region in the image comprises searching for a region in theimage that is indicative of one of a plurality of different types ofpathology by processing the received image data using the learningalgorithm, the learning algorithm being trained on image data definingimages of the portion of unhealthy eyes each having a respective one ofthe different types of pathology; and in case a region in the image thatis indicative of one of the plurality of different types of pathology isfound in the searching: the method further comprises selecting, for theone of the plurality of different types of pathology, a respective oneof a plurality of different types of measurement modality for ameasurement to be performed on the eye; and the method comprisesgenerating, as the instruction for the eye measurement apparatus toperform the measurement on the portion of the eye, an instruction for aneye measurement apparatus of the selected measurement modality toperform the measurement on the portion of the eye, using the referencepoint for setting the location of the measurement on the portion of theeye.
 2. The computer-implemented method of claim 1, wherein: thereceived image data is image data of a first imaging modality; the eyemeasurement apparatus is configured to perform, as the measurement onthe portion of the eye, an image capture process of a second imagingmodality to image a region in the portion of the eye, and to acquire, asthe measurement data, image data of the second imaging modality, thesecond imaging modality being different to the first imaging modality;in a case where the one of the plurality of different types of pathologyis glaucoma, the method comprises generating an instruction for the eyemeasurement apparatus to perform, as the image capture process of thesecond imaging modality, an optical coherence tomography, OCT, scan ofthe region in the portion of the eye, and to acquire, as the measurementdata, image data of the OCT scan; in a case where the one of theplurality of different types of pathology is severe diabeticretinopathy, the method comprises generating an instruction for the eyemeasurement apparatus to perform, as the image capture process of thesecond imaging modality, an optical coherence tomography, OCT, scan of aregion of a retina of the eye, and to acquire, as the measurement data,image data of the OCT scan; in a case where the one of the plurality ofdifferent types of pathology is a tumour, the method comprisesgenerating an instruction for the eye measurement apparatus to perform,as the image capture process of the second imaging modality, ahigh-density optical coherence tomography, OCT, B-scan of the region inthe portion of the eye, and to acquire, as the measurement data, imagedata of the high-density OCT B-scan; in a case where the one of theplurality of different types of pathology is drusen, the methodcomprises generating an instruction for the eye measurement apparatus toperform, as the image capture process of the second imaging modality, anoptical coherence tomography, OCT, B-scan of the region in the portionof the eye, and to acquire, as the measurement data, image data of theOCT B-scan; in a case where the one of the plurality of different typesof pathology is oedema or atrophy, the method comprises generating aninstruction for the eye measurement apparatus to perform, as the imagecapture process of the second imaging modality, an optical coherencetomography, OCT, scan of the region in the portion of the eye, and toacquire, as the measurement data, image data of the OCT scan.
 3. Thecomputer-implemented method of claim 1, further comprising generatinginstructions for controlling a display unit to display the location ofthe region in the image of the portion of the eye and a representationof the received measurement data.
 4. A non-transitory computer-readablestorage medium storing a computer program which, when executed by aprocessor, causes the processor to execute a computer-implemented methodaccording to claim
 1. 5. A computer-implemented method of searching forthe presence of a pathology in an image of a portion of an eye acquiredby an ocular imaging system, the method comprising: receiving image datadefining the image; searching for the presence of at least one of aplurality of different types of pathology in the image by processing thereceived image data using a learning algorithm trained on both firstimage data and second image data, the first image data defining imagesof healthy eyes, and the second image data defining images of unhealthyeyes each having a respective one of the different types of pathology;and in case at least one of the plurality of different types ofpathology is found to be present in the image: selecting, for each of atleast one type of pathology found to be present in the image, arespective one of a plurality of different types of measurement modalitywhich is to be used to perform a measurement on the portion of the eye;generating, for each of the at least one type of pathology found to bepresent in the image, a respective instruction for an eye measurementapparatus of the respective selected measurement modality to perform themeasurement on the portion of the eye; and receiving measurement data ofthe measurement performed by the eye measurement apparatus of eachselected measurement modality.
 6. The computer-implemented method ofclaim 5, further comprising, in case at least one of the plurality ofdifferent types of pathology is found to be present in the image: foreach of the at least one of the different types of pathology found to bepresent in the image, searching for a respective region in the imagethat is indicative of the respective type of pathology, by processingthe received image data using the learning algorithm, and recording alocation of the respective region in the image, wherein a respectiveinstruction for the eye measurement apparatus of each selectedmeasurement modality to perform a measurement on the eye using areference point for locating the measurement which is based on therespective recorded location is generated.
 7. The computer-implementedmethod of claim 6, wherein, in case at least one of the plurality ofdifferent types of pathology is found to be present in the image, foreach of the at least one of the different types of pathology found to bepresent in the image: a respective one of a plurality of different typesof imaging modality which is to be used to image the portion of the eyeis selected as the respective one of the plurality of different types ofmeasurement modality; a respective instruction for an eye measurementapparatus of the selected imaging modality to image the portion of theeye, using a reference point for locating a region of the eye to beimaged which is based on the respective recorded location, is generatedas the respective instruction for the eye measurement apparatus of theselected measurement modality; and respective image data of the imagingperformed by the eye measurement apparatus of the selected imagingmodality is received as the measurement data of the measurementperformed by the eye measurement apparatus.
 8. The computer-implementedmethod of claim 7, wherein: in a case where one of the plurality ofdifferent types of pathology found to be present in the image isglaucoma, the method comprises generating, as the respective instructionfor the eye measurement apparatus of the selected imaging modality toimage the portion of the eye, an instruction for the eye measurementapparatus to perform an optical coherence tomography, OCT, scan of theregion in the portion of the eye, and to acquire, as the measurementdata, image data of the OCT scan; in a case where one of the pluralityof different types of pathology found to be present in the image issevere diabetic retinopathy, the method comprises generating, as therespective instruction for the eye measurement apparatus of the selectedimaging modality to image the portion of the eye, an instruction for theeye measurement apparatus to perform an optical coherence tomography,OCT, scan of a region of a retina of the eye, and to acquire, as themeasurement data, image data of the OCT scan; in a case where one of theplurality of different types of pathology found to be present in theimage is a tumour, the method comprises generating, as the respectiveinstruction for the eye measurement apparatus of the selected imagingmodality to image the portion of the eye, an instruction for the eyemeasurement apparatus to perform a high-density optical coherencetomography, OCT, B-scan of the region in the portion of the eye, and toacquire, as the measurement data, image data of the high-density OCTB-scan; in a case where the one of the plurality of different types ofpathology found to be present in the image is drusen, the methodcomprises generating, as the respective instruction for the eyemeasurement apparatus of the selected imaging modality to image theportion of the eye, an instruction for the eye measurement apparatus toperform an optical coherence tomography, OCT, B-scan of the region inthe portion of the eye, and to acquire, as the measurement data, imagedata of the OCT B-scan; and in a case where the one of the plurality ofdifferent types of pathology found to be present in the image is oedemaor atrophy, the method comprises generating, as the respectiveinstruction for the eye measurement apparatus of the selected imagingmodality to image the portion of the eye, an instruction for the eyemeasurement apparatus to perform an optical coherence tomography, OCT,scan of the region in the portion of the eye, and to acquire, as themeasurement data, image data of the OCT scan.
 9. Thecomputer-implemented method of claim 5, wherein, in case at least one ofthe plurality of different types of pathology is found to be present inthe image, at least one of the instructions generated is an instructionfor an eye measurement apparatus of a selected measurement modality tomeasure a functional response of the eye to light stimulation.
 10. Thecomputer-implemented method of claim 5, further comprising generatinginstructions for controlling a display unit to display, for each of theat least one of the different types of pathology found to be present inthe image, the recorded location of the respective region in the imageof the portion of the eye and a representation of the receivedmeasurement data.
 11. An apparatus for searching for a region indicativeof a pathology in an image of a portion of an eye acquired by an ocularimaging system, the apparatus comprising: a receiver module configuredto receive image data defining the image; a search module configured tosearch for the region in the image by processing the received image datausing a learning algorithm trained on both first image data and secondimage data, the first image data defining images of the portion ofhealthy eyes, and the second image data defining images of the portionof unhealthy eyes each having at least one region that is indicative ofthe pathology; and an instruction generating module configured toperform, in response to a region in the image that is indicative of thepathology being found by the search module, processes of: determining alocation of the region in the image; and generating an instruction foran eye measurement apparatus to perform a measurement on the portion ofthe eye to generate measurement data, using a reference point based onthe determined location for setting a location of the measurement on theportion of the eye, wherein the receiver module is further configured toreceive the measurement data from the eye measurement apparatus, whereinthe search module is configured to search for the region in the image bysearching for a region in the image that is indicative of one of aplurality of different types of pathology by processing the receivedimage data using the learning algorithm, the learning algorithm beingtrained on image data defining images of the portion of unhealthy eyeseach having a respective one of the different types of pathology, theinstruction generating module is configured to perform, in response to aregion in the image that is indicative of one of the plurality ofdifferent types of pathology being found by the search module, processesof: selecting, for the one of the plurality of different types ofpathology, a respective one of a plurality of different types ofmeasurement modality for a measurement to be performed on the eye; andgenerating, as the instruction for the eye measurement apparatus toperform the measurement on the portion of the eye, an instruction for aneye measurement apparatus of the selected measurement modality toperform the measurement on the portion of the eye, using the referencepoint for setting the location of the measurement on the portion of theeye.
 12. An apparatus for searching for the presence of a pathology inan image of a portion of an eye acquired by an ocular imaging system,the apparatus comprising: a receiver module configured to receive imagedata defining the image; a search module configured to search for thepresence of at least one of a plurality of different types of pathologyin the image by processing the received image data using a learningalgorithm trained on both first image data and second image data, thefirst image data defining images of healthy eyes, and the second imagedata defining images of unhealthy eyes each having a respective one ofthe different types of pathology; and an instruction generating moduleconfigured to perform, in response to at least one of the plurality ofdifferent types of pathology being found to be present in the image bythe search module, processes of: selecting, for each of at least onetype of pathology found to be present in the image, a respective one ofa plurality of different types of measurement modality which is to beused to perform a measurement on the portion of the eye; and generating,for each of the at least one type of pathology found to be present inthe image, a respective instruction for an eye measurement apparatus ofthe respective selected measurement modality to perform the measurementon the portion of the eye, wherein the receiver module is furtherconfigured to receive measurement data of the measurement performed bythe eye measurement apparatus of each selected measurement modality.